Through this 100% online Postgraduate certificate, you will gain new skills to perform Image Transformations and increase variability in training data"

Transfer Learning refers to a grouping of methods with which a model developed for a specific task can be used as a starting point for the execution of a different task.  For example, pre-trained systems in medical imaging (such as MRI) can be transferred or tuned to diagnose specific diseases such as cancer. This makes this technique a valuable tool in the arsenal of Deep Learning specialists, as well as reducing both the time and resources needed to train models and improve generalization on small datasets.  

In this context, TECH develops a revolutionary program on Deep Neural Network Training in Deep Learning. Designed by experts in this field, the curriculum will delve into aspects such as feature extraction, reuse of pre-trained layers or learning rate scheduling. In this way, practitioners will enrich their daily practice with state-of-the-art methods to ensure the effectiveness of neural architectures. The curriculum will also provide students with practical guidelines including the selection of metrics, evaluation parameters and hypothesis testing. The program will also address regularization procedures, in order for students to adequately prevent overfitting in Neural Network models.

On the other hand, the university program has a totally online format, easy to access from any device with an Internet connection and without predetermined schedules. Therefore, specialists will be able to perfectly combine their studies with the rest of their daily obligations.  Along the same lines, TECH is based on the cutting-edge Relearning teaching method. This consists of the progressive repetition of key content, so that students experience natural and effective learning without the need to resort to techniques that involve extra effort, such as traditional memorization. 

TECH offers you the most complete and updated scientific program on the market. You will be an expert in Deep Learning!"

This Postgraduate certificate in Training of Deep Neural Networks in Deep Learning contains the most complete and up-to-date program on the market. The most important features include:

• The development of practical cases presented by experts in Training of Deep Neural Networks in Deep Learning
• The graphic, schematic and practical contents of the program provide Sports and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies 
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Are you looking to specialize in Pretrained Layer Reuse? Achieve it with this university program in just 150 hours”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations. 

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

You will master Data Augmentation to improve the generalization and robustness of Machine Learning models"

You will delve into the modules of this program through the innovative Relearning methodology, incorporating the most complex concepts in a fast and flexible way"

This program aims to provide students with a solid understanding of Deep Learning  and to equip their praxis with the advances that have been made in Deep Neural Network Training. The program will address the most innovative techniques for both gradient optimization and weight initialization. In addition, the curriculum will delve into advanced Evaluation Metrics, aimed at measuring the performance of trained models on a variety of tasks. In addition, the teaching materials will focus on Transfer Learning, enabling graduates to improve efficiency in the use of data and computational resources.

The university program includes case studies to bring you closer to the reality of the labor market, bearing in mind the revolution brought about by Deep Learning"

Module 1. Deep Neural Networks Training

1.1. Gradient Problems

1.1.1. Gradient Optimization Techniques
1.1.2. Stochastic Gradients
1.1.3. Weight Initialization Techniques

1.2. Reuse of Pre-Trained Layers

1.2.1. Learning Transfer Training
1.2.2. Feature Extraction
1.2.3. Deep Learning

1.3. Optimizers

1.3.1. Stochastic Gradient Descent Optimizers
1.3.2. Adam and RMSprop Optimizers
1.3.3. Moment Optimizers

1.4. Learning Rate Programming

1.4.1. Automatic Learning Rate Control
1.4.2. Learning Cycles
1.4.3. Smoothing Terms

1.5. Overfitting

1.5.1. Cross Validation
1.5.2. Regularization
1.5.3. Evaluation Metrics

1.6. Practical Guidelines

1.6.1. Model Design
1.6.2. Selection of Metrics and Evaluation Parameters
1.6.3. Hypothesis Testing

1.7. Transfer Learning

1.7.1. Learning Transfer Training
1.7.2. Feature Extraction
1.7.3. Deep Learning

1.8. Data Augmentation

1.8.1. Image Transformations
1.8.2. Synthetic Data Generation
1.8.3. Text Transformation

1.9. Practical Application of Transfer Learning

1.9.1. Learning Transfer Training
1.9.2. Feature Extraction
1.9.3. Deep Learning

1.10. Regularization

1.10.1. L1 and L2
1.10.2. Regularization by Maximum Entropy
1.10.3. Dropout

You will have a library of learning resources available 24 hours a day to ensure that your learning is successful”